This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Recently developed diffusion weighted imaging (DWI) technology provides an unprecedented view of anatomical connectivity in the living human brain. The most popular DWI modeling method is diffusion tensor imaging (DTI). Unfortunately, this method is severely limited in its ability to resolve white matter tract crossings, which occur quite often in the human brain (Alexander et al., 2002). We will use advanced DWI modeling methods, such as PAS-MRI, Q-Ball, and probabilistic tractography, to more accurately resolve white matter tract crossings and anatomical connectivity between brain regions. We plan to use supercomputing resources mostly for the computationally intensive PAS-MRI algorithm (based on maximum-entropy spherical deconvolution;Jansons et al., 2003 and Alexander et al., 2005). Resolving white matter tract crossings with this algorithm takes one minute per voxel on modern desktop computers. With 786432 voxels (128 X 128 in-plane, and 48 slices) it would take 546 days to compute just one subjects white matter tractography. We hope using supercomputing resources will make this a more practical endeavor.